Reducing the Scope of Load Test Analysis
Load testing execution produces a huge amount of data. Investigation and analysis are time-consuming, and numbers tend to hide important information about issues and trends. using machine learning is a good way to solve data issues by giving meaningful insights about what happened during test execution. Julio Cesar de Lima Costa will show you how to use K-means clustering, a machine learning algorithm, to reduce almost 300,000 records to fewer than 1,000 and still get good insights into load testing results. He will explain K-means clustering, detail what use cases and applications this method can be used in, and give the steps to help you reproduce a K-means clustering experiment in your own projects. You'll learn how to use this machine learning algorithm to reduce the scope of your load testing and getting meaningful analysis from your data faster.